# Hyperspectral and Multispectral Image Fusion based on a Sparse Representation

arXiv: https://arxiv.org/abs/1409.5729 · HTML: https://mlsanity.com/paper/1409.5729/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2014.

## Built on by (most cited first, 23 of 23)

- [Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model](https://mlsanity.com/paper/2105.10196/) (2021)
- [HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening](https://mlsanity.com/paper/2203.02503/) (2022)
- [HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models](https://mlsanity.com/paper/2306.12085/) (2023)
- [Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction](https://mlsanity.com/paper/2107.02630/) (2021)
- [Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2110.11591/) (2021)
- [Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2111.13923/) (2021)
- [Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review](https://mlsanity.com/paper/2205.06407/) (2022)
- [Hyperspectral Pansharpening: Critical Review, Tools and Future Perspectives](https://mlsanity.com/paper/2407.01355/) (2024)
- [HSR-KAN: Efficient Hyperspectral Image Super-Resolution via Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2409.06705/) (2024)
- [Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral Imagery](https://mlsanity.com/paper/2103.04354/) (2021)
- [Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings](https://mlsanity.com/paper/2103.10614/) (2021)
- [Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy](https://mlsanity.com/paper/2103.12165/) (2021)
- [Guided Colorization Using Mono-Color Image Pairs](https://mlsanity.com/paper/2108.07471/) (2021)
- [Multispectral image fusion based on super pixel segmentation](https://mlsanity.com/paper/2112.11329/) (2021)
- [PIF-Net: Ill-Posed Prior Guided Multispectral and Hyperspectral Image Fusion via Invertible Mamba and Fusion-Aware LoRA](https://mlsanity.com/paper/2508.00453/) (2025)
- [Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing](https://mlsanity.com/paper/2103.01449/) (2021)
- [Spatiotemporal Fusion in Remote Sensing](https://mlsanity.com/paper/2107.02701/) (2021)
- [Guided Deep Generative Model-based Spatial Regularization for Multiband Imaging Inverse Problems](https://mlsanity.com/paper/2306.17197/) (2023)
- [Unsupervised Hyperspectral and Multispectral Images Fusion Based on the Cycle Consistency](https://mlsanity.com/paper/2307.03413/) (2023)
- [A Hybrid Registration and Fusion Method for Hyperspectral Super-resolution](https://mlsanity.com/paper/2407.05279/) (2024)
- [Degradation Alchemy: Self-Supervised Unknown-to-Known Transformation for Blind Hyperspectral Image Fusion](https://mlsanity.com/paper/2503.14892/) (2025)
- [SpecSwin3D: Generating Hyperspectral Imagery from Multispectral Data via Transformer Networks](https://mlsanity.com/paper/2509.06122/) (2025)
- [Hyperspectral Super-Resolution with Inter-Image Variability via Degradation-based Low-Rank and Residual Fusion Method](https://mlsanity.com/paper/2511.15052/) (2025)

## Abstract (arXiv)

This paper presents a variational based approach to fusing hyperspectral and multispectral images. The fusion process is formulated as an inverse problem whose solution is the target image assumed to live in a much lower dimensional subspace. A sparse regularization term is carefully designed, relying on a decomposition of the scene on a set of dictionaries. The dictionary atoms and the corresponding supports of active coding coefficients are learned from the observed images. Then, conditionally on these dictionaries and supports, the fusion problem is solved via alternating optimization with respect to the target image (using the alternating direction method of multipliers) and the coding coefficients. Simulation results demonstrate the efficiency of the proposed algorithm when compared with the state-of-the-art fusion methods.